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Title: shiftNMFk 1.1: Robust Nonnegative matrix factorization with kmeans clustering and signal shift, for allocation of unknown physical sources, toy version for open sourcing with publications

Abstract

This code is a toy (short) version of CODE-2016-83. From a general perspective, the code represents an unsupervised adaptive machine learning algorithm that allows efficient and high performance de-mixing and feature extraction of a multitude of non-negative signals mixed and recorded by a network of uncorrelated sensor arrays. The code identifies the number of the mixed original signals and their locations. Further, the code also allows deciphering of signals that have been delayed in regards to the mixing process in each sensor. This code is high customizable and it can be efficiently used for a fast macro-analyses of data. The code is applicable to a plethora of distinct problems: chemical decomposition, pressure transient decomposition, unknown sources/signal allocation, EM signal decomposition. An additional procedure for allocation of the unknown sources is incorporated in the code.

Authors:
 [1];  [1];  [1];  [1]
  1. LANL
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
Contributing Org.:
Los Alamos National Laboratory
OSTI Identifier:
1325758
Report Number(s):
ShiftNMFk 1.1; 004929MLTPL00
C16097
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Software
Software Revision:
00
Software Package Number:
004929
Software CPU:
MLTPL
Open Source:
Yes
Open Source under the BSD License.
Source Code Available:
Yes
Country of Publication:
United States

Citation Formats

Alexandrov, Boian S., Lliev, Filip L., Stanev, Valentin G., and Vesselinov, Velimir. shiftNMFk 1.1: Robust Nonnegative matrix factorization with kmeans clustering and signal shift, for allocation of unknown physical sources, toy version for open sourcing with publications. Computer software. https://www.osti.gov//servlets/purl/1325758. Vers. 00. USDOE. 19 Jul. 2016. Web.
Alexandrov, Boian S., Lliev, Filip L., Stanev, Valentin G., & Vesselinov, Velimir. (2016, July 19). shiftNMFk 1.1: Robust Nonnegative matrix factorization with kmeans clustering and signal shift, for allocation of unknown physical sources, toy version for open sourcing with publications (Version 00) [Computer software]. https://www.osti.gov//servlets/purl/1325758.
Alexandrov, Boian S., Lliev, Filip L., Stanev, Valentin G., and Vesselinov, Velimir. shiftNMFk 1.1: Robust Nonnegative matrix factorization with kmeans clustering and signal shift, for allocation of unknown physical sources, toy version for open sourcing with publications. Computer software. Version 00. July 19, 2016. https://www.osti.gov//servlets/purl/1325758.
@misc{osti_1325758,
title = {shiftNMFk 1.1: Robust Nonnegative matrix factorization with kmeans clustering and signal shift, for allocation of unknown physical sources, toy version for open sourcing with publications, Version 00},
author = {Alexandrov, Boian S. and Lliev, Filip L. and Stanev, Valentin G. and Vesselinov, Velimir},
abstractNote = {This code is a toy (short) version of CODE-2016-83. From a general perspective, the code represents an unsupervised adaptive machine learning algorithm that allows efficient and high performance de-mixing and feature extraction of a multitude of non-negative signals mixed and recorded by a network of uncorrelated sensor arrays. The code identifies the number of the mixed original signals and their locations. Further, the code also allows deciphering of signals that have been delayed in regards to the mixing process in each sensor. This code is high customizable and it can be efficiently used for a fast macro-analyses of data. The code is applicable to a plethora of distinct problems: chemical decomposition, pressure transient decomposition, unknown sources/signal allocation, EM signal decomposition. An additional procedure for allocation of the unknown sources is incorporated in the code.},
url = {https://www.osti.gov//servlets/purl/1325758},
doi = {},
year = {2016},
month = {7},
note =
}